In today's rapidly changing business environment, data science plays a significant role. Data science, which allows organizations to analyze complex data and derive actionable insights, is the foundation of marketing and business decision-making.
Against this background, the word "data science" has been heard more and more frequently in recent years. However, the concept of data science may still be difficult to grasp, especially for business people with a liberal arts background.

What is data science? What does it mean for business? --In this article, we will explain the basic concepts and terminology of data science, application examples in business and marketing, and challenges and solutions in data science in an easy-to-understand manner from a business perspective. Get ready for the data-driven era by learning the knowledge, skills, and techniques you need to apply data science to your business.

Introduction: Explaining complex concepts in an easy-to-understand manner

“Data Science” made simple

Data science is the knowledge and technology of statistics and information engineering required to collect, organize, analyze, and visualize data. It is an approach that aims to derive knowledge and insights useful for business from huge and complex data such as big data.

As an example, imagine you have a giant puzzle. Data science is like the process of putting pieces together to reveal a picture of a puzzle.

“Machine learning” made simple

Machine learning is the practice of training computers to learn from experience in the same way humans do. This allows the system to automatically improve, make predictions and decisions based on data without any programming.

Machine learning is easier to understand if you think of it like training a pet. In the same way that pet training allows a pet to perform a specific behavior on its own without commands, it is possible to teach a computer to learn from experience.

“Algorithm” made simple

An algorithm is a procedure or rule that a computer follows to solve a problem or perform a task.

Think of algorithms like cooking recipes. A recipe consists of "ingredients and their quantities" and "procedures for processing and cooking the ingredients." In the computer world, "ingredients and their quantities" are "data," and "cooking procedures" are "cooking procedures." algorithm”. Just as you follow the steps in a recipe to make a desired dish, your computer also follows an algorithm that guides you through the process to complete the task.

“Big Data” made simple

Big data refers to the vast and complex collection of data generated around the world. It is like an ocean of data that is generated through people's daily lives and businesses, and it is a scale of data that is difficult to process with general databases and management tools. Analyzing big data requires special techniques and tools using data science.

To understand big data, imagine a huge library. There are more books in libraries than it would take a lifetime to read, but by using data science, we can efficiently search for books and find valuable information from this huge collection. You will be able to do this.

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Predictive analysis made simple

Predictive analytics is the use of data, algorithms, machine learning, etc. to predict future outcomes.

Weather forecasting is a familiar example of predictive analysis. Weather forecasting predicts future weather conditions by analyzing past weather patterns.

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Basics of data science

Understand data and information

The words "data" and "information" are often used interchangeably, but they actually have different meanings. Understanding the difference in meaning between the two is the basis of data science.

"data"is a raw (raw) fact or phenomenon expressed in the form of numbers, strings, etc. Data alone has no meaning. Only when data is analyzed and processed can it be given meaning and value.

"information"is data that is given meaning and value. By turning data into information, it can be used to help people understand and make decisions.

Data science can be said to be a field of study that uses techniques such as statistics, algorithms, and machine learning to extract the "information" needed to solve problems from large amounts of complex big "data."

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Main components of data science

The main elements of data science include "business ability," "statistics knowledge," "information engineering skills," and "data visualization ability." Let's discuss these elements.

"Business power"refers to the skills necessary to understand and solve problems, such as logical thinking, problem definition, understanding data from a business perspective, and evaluating the validity of analytical methods. These skills will also be necessary to implement data science into your business.

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"statistics"is a mathematical method for gaining insight into data. By analyzing huge amounts of data and determining its characteristics, regularity/irregularity, and relationships between data, it is useful for understanding the characteristics of data and predicting the future.

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"Information Engineering"is a field that uses the power of information in engineering, and is related to programming, algorithms, databases, etc. Due to the nature of handling tools necessary for data processing, management, and analysis, knowledge and skills related to system development and design are also required.

"Data visualization power"is the ability to visualize and communicate data in the form of diagrams, charts, etc. using graphs, dashboards, infographics, etc.

By combining knowledge and techniques from these fields, data science can extract the knowledge and insights needed to solve problems from large-scale datasets.

Application examples in business and marketing

Enhanced decision making

Data science enables management and marketing to make decisions based on evidence rather than just intuition. By making strategy planning data-driven, you can increase your chances of success.

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Customer segmentation and targeting

Segmenting customers based on data analysis enables personalized marketing. As a result, you can increase customer engagement and customer satisfaction.

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Optimize marketing budget allocation

By utilizing data science, it is possible to predict how to allocate budget for various marketing measures to achieve maximum results. For example, by using a method called marketing mix modeling (MMM), it is possible to measure the contribution and ROI of each marketing measure to results and calculate the optimal allocation of budget.

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Challenges and solutions in data science

Here, we introduce the challenges that businesses often face when using data science. Identifying and addressing these challenges is the key to success with data science.

Challenges in data collection, organization, and management

To perform data science, it is important to appropriately collect, organize, and manage the necessary data. However, as the amount of data increases and the types of data become more diverse, the quality and reliability of the data may deteriorate. Effective solutions to these challenges include data cleansing, standardization, and appropriate data storage. It is also important to encourage data sharing and collaboration between departments to prevent "organizational silos" that make data collection and management difficult.

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Challenges in data analysis, interpretation, and utilization

The purpose of data science is to extract useful knowledge and insights from data and use them for business purposes. Analyzing and interpreting data often requires advanced skills and techniques, and utilizing data also requires communication and presentation skills. To address these challenges, we need to provide education and training on data science, share best practices and know-how on data utilization, collaborate with data scientists and other experts, and use techniques such as data visualization and storytelling. Utilization is effective.

Legal/security issues

Depending on the type of data utilized, it may violate individual privacy and rights. In addition, if management is insufficient, there are security risks such as data leakage and falsification. It is important to comply with ethical standards and laws regarding the collection, management and use of data, to take security measures such as data encryption and backup, and to thoroughly explain the use of data and obtain consent from data providers.

Risks of over-reliance on data

Decision-making based on data can vary greatly depending on the quality and quantity of data, the purpose and method of analysis, and how the results are interpreted. It is important not to be overconfident in data, but to always question and verify.

Over-reliance on data can lead to data bias, missing data, and getting caught up in past trends and patterns. To prevent this, it is effective to expand the scope of data collection and analysis (in other words, increase the diversity and novelty of the data). For example, you may want to consider using external data as well as internal data, or incorporating qualitative data as well as quantitative data.

Consider leaving it to a professional company

Utilizing data science requires a certain level of specialized knowledge and skills, so depending on the case, it may be effective to leave it to an external professional company.

Let's take a look at the advantages and disadvantages of outsourcing data science execution.

The benefits include:

• No need to hire or train personnel with specialized skills and knowledge
• Project execution and quality can be entrusted to external companies familiar with data science.
• Gain insights and approaches from new perspectives that are often overlooked internally.

And the disadvantages include:

• It is expensive to use the service (however, it is necessary to compare it with the cost of in-house production)
• When using the service, it is necessary to provide your company's data to an external company.
• Communication with external companies takes time and effort as deep business understanding is often required.

It is important to weigh the pros and cons, taking into consideration your company's needs, budget, data security, etc. Please read the article below to learn about the advantages and disadvantages of going in-house.
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To the end

Data science is an essential discipline for business. By correctly understanding and effectively utilizing data science, it is possible to solve various business issues. However, data science involves a variety of technologies and methods, and it may seem difficult for business people to understand. This article provides an easy-to-understand explanation of the basic concepts and terminology of data science, examples of its application in business and marketing, and common issues and solutions from a business perspective.

XICA has been providing services in the data science field in marketing for over 10 years, and has a track record of supporting over 250 companies, mainly domestic enterprise companies.

Data scientists and consultants with rich and deep expertise in a wide range of industries utilize data science.Develop a data-driven strategyFromCustomer understanding,Creative analysis/production,Media Planning,Effectiveness verification and budget optimizationWe support the use of data to support better decision-making. If you would like to consult with us about the most suitable data science utilization support according to your company's unique issues and objectives, as well as the data you have, please contact us.Contact us.

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Marketing team of XICA Co., Ltd.